An R package for the dependent-adjusted Benjamini-Hochberg and step-up procedures
This R package implements the dependent-adjusted Benjamini-Hochberg procedure, Benjamini-Yekutieli procedure, and step-up procedures for parametric and non-parametric multiple testing problems with exact false discovery rate control. The procedures are proposed in our paper: Conditional calibration for false discovery rate control under dependence. The current version supports one- and two-sided tests on multivariate z-statistics (dBH_mvgauss), multivariate t-statistis (dBH_mvt), and fixed-design homoscedastic Gaussian linear models (dBH_lm). The procedures for other problems will be added in the future versions.
if (!require("devtools")){
install.packages("devtools")
}
devtools::install_github("lihualei71/dbh")
We illustrate the usage of dBH_mvgauss below. For details please read the manual.
library(dbh)
## basic example code
# Generate mu and Sigma for an AR process
n <- 100
rho <- 0.8
Sigma <- rho^(abs(outer(1:n, 1:n, "-")))
mu1 <- 2.5
nalt <- 10
mu <- c(rep(mu1, nalt), rep(0, n - nalt))
# Generate the z-values
set.seed(1)
zvals <- rep(NA, n)
zvals[1] <- rnorm(1)
for (i in 2:n){
zvals[i] <- zvals[i - 1] * rho + rnorm(1) * sqrt(1 - rho^2)
}
zvals <- zvals + mu
# Run dBH_1(\alpha) for one-sided tests
alpha <- 0.05
res <- dBH_mvgauss(zvals = zvals, Sigma = Sigma, side = "right", alpha = alpha,
gamma = 1, niter = 1, avals_type = "BH")
# Run dBH_1(\alpha) for two-sided tests
res <- dBH_mvgauss(zvals = zvals, Sigma = Sigma, side = "right", alpha = alpha,
gamma = 1, niter = 1, avals_type = "BH") 
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